The present invention relates to a three-dimensional (3D) object recognition system that classifies an object using expanding global region of interest geometric features. While methods for 3D object recognition exist, existing methods are very different than the present invention. The existing methods are based on variants of spin images or 3D shape contexts. Neither of the existing approaches take advantage of the difference in z and x-y variations of natural and artificial physical objects that are naturally tied to the ground plane. For example, spin images require the estimation of accurate normal vectors on a surface mesh enclosing the object. Further, normal vectors are sensitive to noise and are inaccurate unless the sampling density is high. Additionally, spin images also need to be defined on a set of points on the object. Using a large number of points and large domains for each point results in memorization of the training objects rather than learning, resulting in “brittle” classifiers. This noise sensitivity and brittleness means that recognition systems based on spin images do not work well if the objects exhibit large intra-class variability, the spatial sampling is not high, or if noise is present.
Alternatively, 3D shape contexts use a feature vector which is essentially a 3D histogram of the point cloud data. The histogram bins are 3D angular sector volumes that form a sphere centered on the object or on points of interest on the object. 3D shape contexts do not utilize the geometric information encoded in the point cloud data. Instead, the histogram bins describe how many points are present in a spatial region. Unlike the present invention, the shape context histogram bins are not invariant to rotation of the object or local object region in azimuth.
Thus, a continuing need exists for a 3D object recognition system that is invariant to rotation of the object or rotation of a local object region in azimuth.
The present invention solves this need by using rotationally invariant (in azimuth angle) geometric relationships between region of interest (ROI) volumes and contiguous object parts intersected by or contained within the ROI. The resulting features or shape descriptors contain higher level information about the 3D objects as compared to prior art. While the prior art encodes low level information about the locations of points in 3D space, the present invention encodes the higher level geometric properties of the objects and their components without the need for calculating surface meshes or 3D models of the objects. This reduces the number of training examples required and improves classification robustness relative to prior art.